Deep-learning cardiac motion analysis for human survival prediction View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-02

AUTHORS

Ghalib A Bello, Timothy J W Dawes, Jinming Duan, Carlo Biffi, Antonio de Marvao, Luke S G E Howard, J Simon R Gibbs, Martin R Wilkins, Stuart A Cook, Daniel Rueckert, Declan P O'Regan

ABSTRACT

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p = .0012) for our model C=0.75 (95% CI: 0.70 - 0.79) than the human benchmark of C=0.59 (95% CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival. More... »

PAGES

95

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s42256-019-0019-2

DOI

http://dx.doi.org/10.1038/s42256-019-0019-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112058164

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30801055


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Imperial College London", 
          "id": "https://www.grid.ac/institutes/grid.7445.2", 
          "name": [
            "MRC London Institute of Medical Sciences, Imperial College London,UK."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bello", 
        "givenName": "Ghalib A", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imperial College London", 
          "id": "https://www.grid.ac/institutes/grid.7445.2", 
          "name": [
            "MRC London Institute of Medical Sciences, Imperial College London,UK.", 
            "National Heart and Lung Institute, Imperial College London, UK."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Dawes", 
        "givenName": "Timothy J W", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imperial College London", 
          "id": "https://www.grid.ac/institutes/grid.7445.2", 
          "name": [
            "MRC London Institute of Medical Sciences, Imperial College London,UK.", 
            "Department of Computing, Imperial College London, UK."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Duan", 
        "givenName": "Jinming", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imperial College London", 
          "id": "https://www.grid.ac/institutes/grid.7445.2", 
          "name": [
            "MRC London Institute of Medical Sciences, Imperial College London,UK.", 
            "Department of Computing, Imperial College London, UK."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Biffi", 
        "givenName": "Carlo", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imperial College London", 
          "id": "https://www.grid.ac/institutes/grid.7445.2", 
          "name": [
            "MRC London Institute of Medical Sciences, Imperial College London,UK."
          ], 
          "type": "Organization"
        }, 
        "familyName": "de Marvao", 
        "givenName": "Antonio", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imperial College Healthcare NHS Trust", 
          "id": "https://www.grid.ac/institutes/grid.417895.6", 
          "name": [
            "Imperial College Healthcare NHS Trust, London, UK."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Howard", 
        "givenName": "Luke S G E", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imperial College Healthcare NHS Trust", 
          "id": "https://www.grid.ac/institutes/grid.417895.6", 
          "name": [
            "National Heart and Lung Institute, Imperial College London, UK.", 
            "Imperial College Healthcare NHS Trust, London, UK."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gibbs", 
        "givenName": "J Simon R", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imperial College London", 
          "id": "https://www.grid.ac/institutes/grid.7445.2", 
          "name": [
            "Division of Experimental Medicine, Department of Medicine, Imperial College London, UK."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wilkins", 
        "givenName": "Martin R", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imperial College London", 
          "id": "https://www.grid.ac/institutes/grid.7445.2", 
          "name": [
            "MRC London Institute of Medical Sciences, Imperial College London,UK.", 
            "National Heart and Lung Institute, Imperial College London, UK.", 
            "National Heart Centre Singapore, Singapore, and Duke-NUS Graduate Medical School, Singapore."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cook", 
        "givenName": "Stuart A", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imperial College London", 
          "id": "https://www.grid.ac/institutes/grid.7445.2", 
          "name": [
            "Department of Computing, Imperial College London, UK."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Rueckert", 
        "givenName": "Daniel", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imperial College London", 
          "id": "https://www.grid.ac/institutes/grid.7445.2", 
          "name": [
            "MRC London Institute of Medical Sciences, Imperial College London,UK."
          ], 
          "type": "Organization"
        }, 
        "familyName": "O'Regan", 
        "givenName": "Declan P", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s10278-013-9604-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002405103", 
          "https://doi.org/10.1007/s10278-013-9604-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10278-013-9604-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002405103", 
          "https://doi.org/10.1007/s10278-013-9604-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.csbj.2016.11.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004091080"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.media.2013.04.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004187867"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btr597", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005259061"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2016161315", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009835081"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circulationaha.114.010637", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011345305"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circulationaha.114.010637", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011345305"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btr511", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012366359"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2014.07.979", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013047510"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circimaging.114.002107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017679243"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circimaging.114.002107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017679243"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/aje/kwu140", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017703766"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/eurheartj/ehv317", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019762568"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.media.2015.08.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021754446"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jbi.2016.10.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028115945"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0110243", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031720838"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/sim.4780140108", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033373649"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-0-85729-057-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035503987", 
          "https://doi.org/10.1007/978-0-85729-057-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-0-85729-057-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035503987", 
          "https://doi.org/10.1007/978-0-85729-057-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-016-4217-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035858680", 
          "https://doi.org/10.1007/s00330-016-4217-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-016-4217-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035858680", 
          "https://doi.org/10.1007/s00330-016-4217-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-51237-2_2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036650911", 
          "https://doi.org/10.1007/978-3-319-51237-2_2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/(sici)1097-0258(19960229)15:4<361::aid-sim168>3.0.co;2-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037104179"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jama.1982.03320430047030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038627387"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1155/2016/6795352", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038688614"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1532-429x-15-35", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041103373", 
          "https://doi.org/10.1186/1532-429x-15-35"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-46478-7_51", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048275737", 
          "https://doi.org/10.1007/978-3-319-46478-7_51"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-20309-6_1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051381581", 
          "https://doi.org/10.1007/978-3-319-20309-6_1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1532-429x-15-91", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052338964", 
          "https://doi.org/10.1186/1532-429x-15-91"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01621459.1983.10477973", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058302834"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/eurheartj/ehv510", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059576710"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/42.796284", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061170839"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.7326/m14-0698", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1073742342"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature21056", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1074217286", 
          "https://doi.org/10.1038/nature21056"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature21056", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1074217286", 
          "https://doi.org/10.1038/nature21056"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature21056", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1074217286", 
          "https://doi.org/10.1038/nature21056"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/gigascience/gix005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083568878"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/gigascience/gix005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083568878"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.healun.2017.02.016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083902372"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1146/annurev-bioeng-071516-044442", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084228312"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1183/16000617.0108-2016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084249681"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1183/16000617.0108-2016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084249681"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1183/16000617.0108-2016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084249681"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.15420/cfr.2016:25:2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085310347"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.media.2017.06.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1086028023"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10741-017-9621-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1086044150", 
          "https://doi.org/10.1007/s10741-017-9621-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10741-017-9621-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1086044150", 
          "https://doi.org/10.1007/s10741-017-9621-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacbts.2016.11.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1086367886"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/3071178.3071208", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1090598380"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0180944", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1090670297"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.media.2017.07.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1090904008"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circresaha.117.311312", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091132987"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circresaha.117.311312", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091132987"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41598-017-11817-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091665734", 
          "https://doi.org/10.1038/s41598-017-11817-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41598-017-12539-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091866095", 
          "https://doi.org/10.1038/s41598-017-12539-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1155/2017/1279486", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092232528"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijcard.2017.10.106", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092460981"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/3123266.3130141", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092535212"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/222208", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092851007"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/222208", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092851007"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/222208", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092851007"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/hco.0000000000000491", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093120736"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/hco.0000000000000491", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093120736"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icnn.1995.488968", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093669333"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2017.173", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095849007"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.4018/978-1-60566-900-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1096031471"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12874-018-0482-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101228878", 
          "https://doi.org/10.1186/s12874-018-0482-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12874-018-0482-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101228878", 
          "https://doi.org/10.1186/s12874-018-0482-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12874-018-0482-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101228878", 
          "https://doi.org/10.1186/s12874-018-0482-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12874-018-0482-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101228878", 
          "https://doi.org/10.1186/s12874-018-0482-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rsif.2017.0387", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103153059"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rsif.2017.0387", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103153059"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rsif.2017.0387", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103153059"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11063-018-9828-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103170271", 
          "https://doi.org/10.1007/s11063-018-9828-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11063-018-9828-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103170271", 
          "https://doi.org/10.1007/s11063-018-9828-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11063-018-9828-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103170271", 
          "https://doi.org/10.1007/s11063-018-9828-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11063-018-9828-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103170271", 
          "https://doi.org/10.1007/s11063-018-9828-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pcbi.1006076", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103203182"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compeleceng.2018.04.012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103638844"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/ehjci/jey120", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106005374"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/ehjci/jey120", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106005374"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/ehjci/jey120", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106005374"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/ehjci/jey120", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106005374"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12968-018-0471-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106947579", 
          "https://doi.org/10.1186/s12968-018-0471-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12968-018-0471-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106947579", 
          "https://doi.org/10.1186/s12968-018-0471-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12968-018-0471-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106947579", 
          "https://doi.org/10.1186/s12968-018-0471-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12968-018-0471-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106947579", 
          "https://doi.org/10.1186/s12968-018-0471-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-030-00934-2_52", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1107019541", 
          "https://doi.org/10.1007/978-3-030-00934-2_52"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1109705929", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4899-4541-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109705929", 
          "https://doi.org/10.1007/978-1-4899-4541-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4899-4541-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109705929", 
          "https://doi.org/10.1007/978-1-4899-4541-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.2517-6161.1972.tb00899.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110457843"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.2517-6161.1972.tb00899.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110457843"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmi.2019.2894322", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1111636818"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-02", 
    "datePublishedReg": "2019-02-01", 
    "description": "Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p = .0012) for our model C=0.75 (95% CI: 0.70 - 0.79) than the human benchmark of C=0.59 (95% CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/s42256-019-0019-2", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.7441966", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1336255", 
        "issn": [
          "2522-5839"
        ], 
        "name": "Nature Machine Intelligence", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "1"
      }
    ], 
    "name": "Deep-learning cardiac motion analysis for human survival prediction", 
    "pagination": "95", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "5bd7951ac82ce0006dd1abf3f1c2ee591dbcf32dfc2722d80f4be20e5732e850"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30801055"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101740243"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s42256-019-0019-2"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112058164"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s42256-019-0019-2", 
      "https://app.dimensions.ai/details/publication/pub.1112058164"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T10:20", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000348_0000000348/records_54331_00000002.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/s42256-019-0019-2"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s42256-019-0019-2'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s42256-019-0019-2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s42256-019-0019-2'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s42256-019-0019-2'


 

This table displays all metadata directly associated to this object as RDF triples.

345 TRIPLES      21 PREDICATES      93 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s42256-019-0019-2 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N4bf1e0587db04551997ad448aae22be0
4 schema:citation sg:pub.10.1007/978-0-85729-057-1
5 sg:pub.10.1007/978-1-4899-4541-9
6 sg:pub.10.1007/978-3-030-00934-2_52
7 sg:pub.10.1007/978-3-319-20309-6_1
8 sg:pub.10.1007/978-3-319-46478-7_51
9 sg:pub.10.1007/978-3-319-51237-2_2
10 sg:pub.10.1007/s00330-016-4217-6
11 sg:pub.10.1007/s10278-013-9604-9
12 sg:pub.10.1007/s10741-017-9621-8
13 sg:pub.10.1007/s11063-018-9828-2
14 sg:pub.10.1038/nature21056
15 sg:pub.10.1038/s41598-017-11817-6
16 sg:pub.10.1038/s41598-017-12539-5
17 sg:pub.10.1186/1532-429x-15-35
18 sg:pub.10.1186/1532-429x-15-91
19 sg:pub.10.1186/s12874-018-0482-1
20 sg:pub.10.1186/s12968-018-0471-x
21 https://app.dimensions.ai/details/publication/pub.1109705929
22 https://doi.org/10.1001/jama.1982.03320430047030
23 https://doi.org/10.1002/(sici)1097-0258(19960229)15:4<361::aid-sim168>3.0.co;2-4
24 https://doi.org/10.1002/sim.4780140108
25 https://doi.org/10.1016/j.compeleceng.2018.04.012
26 https://doi.org/10.1016/j.csbj.2016.11.001
27 https://doi.org/10.1016/j.healun.2017.02.016
28 https://doi.org/10.1016/j.ijcard.2017.10.106
29 https://doi.org/10.1016/j.jacbts.2016.11.010
30 https://doi.org/10.1016/j.jacc.2014.07.979
31 https://doi.org/10.1016/j.jbi.2016.10.007
32 https://doi.org/10.1016/j.media.2013.04.010
33 https://doi.org/10.1016/j.media.2015.08.009
34 https://doi.org/10.1016/j.media.2017.06.002
35 https://doi.org/10.1016/j.media.2017.07.005
36 https://doi.org/10.1080/01621459.1983.10477973
37 https://doi.org/10.1093/aje/kwu140
38 https://doi.org/10.1093/bioinformatics/btr511
39 https://doi.org/10.1093/bioinformatics/btr597
40 https://doi.org/10.1093/ehjci/jey120
41 https://doi.org/10.1093/eurheartj/ehv317
42 https://doi.org/10.1093/eurheartj/ehv510
43 https://doi.org/10.1093/gigascience/gix005
44 https://doi.org/10.1097/hco.0000000000000491
45 https://doi.org/10.1098/rsif.2017.0387
46 https://doi.org/10.1101/222208
47 https://doi.org/10.1109/42.796284
48 https://doi.org/10.1109/cvpr.2017.173
49 https://doi.org/10.1109/icnn.1995.488968
50 https://doi.org/10.1109/tmi.2019.2894322
51 https://doi.org/10.1111/j.2517-6161.1972.tb00899.x
52 https://doi.org/10.1145/3071178.3071208
53 https://doi.org/10.1145/3123266.3130141
54 https://doi.org/10.1146/annurev-bioeng-071516-044442
55 https://doi.org/10.1148/radiol.2016161315
56 https://doi.org/10.1155/2016/6795352
57 https://doi.org/10.1155/2017/1279486
58 https://doi.org/10.1161/circimaging.114.002107
59 https://doi.org/10.1161/circresaha.117.311312
60 https://doi.org/10.1161/circulationaha.114.010637
61 https://doi.org/10.1183/16000617.0108-2016
62 https://doi.org/10.1371/journal.pcbi.1006076
63 https://doi.org/10.1371/journal.pone.0110243
64 https://doi.org/10.1371/journal.pone.0180944
65 https://doi.org/10.15420/cfr.2016:25:2
66 https://doi.org/10.4018/978-1-60566-900-7
67 https://doi.org/10.7326/m14-0698
68 schema:datePublished 2019-02
69 schema:datePublishedReg 2019-02-01
70 schema:description Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4D<i>survival</i>), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p = .0012) for our model C=0.75 (95% CI: 0.70 - 0.79) than the human benchmark of C=0.59 (95% CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival.
71 schema:genre research_article
72 schema:inLanguage en
73 schema:isAccessibleForFree false
74 schema:isPartOf N5715526dfcd24b62b2690fb3012b2e96
75 Nc68241ddbb744cf0b61c4bd57ad6c066
76 sg:journal.1336255
77 schema:name Deep-learning cardiac motion analysis for human survival prediction
78 schema:pagination 95
79 schema:productId N3441c4e261e14b3cb609d049b3a7b5f1
80 N504d2dbf67034807bccdde23a1f87d2c
81 N6a4275e067eb437681f3327f466e7d70
82 N880263831ef148a2819e0cc500084b8b
83 Nb62e51beb01f41ec92d8b27e3f86df7b
84 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112058164
85 https://doi.org/10.1038/s42256-019-0019-2
86 schema:sdDatePublished 2019-04-11T10:20
87 schema:sdLicense https://scigraph.springernature.com/explorer/license/
88 schema:sdPublisher N183cd5ffc6c04e91991a23df352d588c
89 schema:url https://www.nature.com/articles/s42256-019-0019-2
90 sgo:license sg:explorer/license/
91 sgo:sdDataset articles
92 rdf:type schema:ScholarlyArticle
93 N0e0d99b8204540a3a8ff8624eb4b701b schema:affiliation https://www.grid.ac/institutes/grid.7445.2
94 schema:familyName Biffi
95 schema:givenName Carlo
96 rdf:type schema:Person
97 N183cd5ffc6c04e91991a23df352d588c schema:name Springer Nature - SN SciGraph project
98 rdf:type schema:Organization
99 N3441c4e261e14b3cb609d049b3a7b5f1 schema:name doi
100 schema:value 10.1038/s42256-019-0019-2
101 rdf:type schema:PropertyValue
102 N35ccb34c7fee4593a4305323815b7f83 rdf:first Nf144b0aacf3d44ef8f4c3a94b4ce30ef
103 rdf:rest N9f561351e4ce45deaac5b3bb6527915d
104 N41b50dae1fff447e85bf792efc9d535c schema:affiliation https://www.grid.ac/institutes/grid.417895.6
105 schema:familyName Gibbs
106 schema:givenName J Simon R
107 rdf:type schema:Person
108 N4bf1e0587db04551997ad448aae22be0 rdf:first N565e3d19931c4eb4a026c393388f139b
109 rdf:rest N534e069f33be4d6caffa88a4075ed594
110 N4f78d77e902148519ae57a30eb7b8984 schema:affiliation https://www.grid.ac/institutes/grid.7445.2
111 schema:familyName Dawes
112 schema:givenName Timothy J W
113 rdf:type schema:Person
114 N504d2dbf67034807bccdde23a1f87d2c schema:name dimensions_id
115 schema:value pub.1112058164
116 rdf:type schema:PropertyValue
117 N534e069f33be4d6caffa88a4075ed594 rdf:first N4f78d77e902148519ae57a30eb7b8984
118 rdf:rest N80431d6cddc94c15b98daffdd690c3f1
119 N565e3d19931c4eb4a026c393388f139b schema:affiliation https://www.grid.ac/institutes/grid.7445.2
120 schema:familyName Bello
121 schema:givenName Ghalib A
122 rdf:type schema:Person
123 N5715526dfcd24b62b2690fb3012b2e96 schema:issueNumber 2
124 rdf:type schema:PublicationIssue
125 N576136f5076444aeb36813658c93faa9 schema:affiliation https://www.grid.ac/institutes/grid.7445.2
126 schema:familyName Rueckert
127 schema:givenName Daniel
128 rdf:type schema:Person
129 N58a8c3da7af04038b76a30f0b0b0d37b rdf:first N576136f5076444aeb36813658c93faa9
130 rdf:rest Nabe1c6b756a945a3be183e409d8941cf
131 N6462bf63adda4c0d8bbbea21fb076d43 rdf:first Nfc9a620a75824718b44ca73182d0954b
132 rdf:rest Nb7d6bb3b93f6462c8df1b246705de7a7
133 N6a4275e067eb437681f3327f466e7d70 schema:name readcube_id
134 schema:value 5bd7951ac82ce0006dd1abf3f1c2ee591dbcf32dfc2722d80f4be20e5732e850
135 rdf:type schema:PropertyValue
136 N80431d6cddc94c15b98daffdd690c3f1 rdf:first Nea10e99233304ceebf4c40734b5711c4
137 rdf:rest Ne9c256c106e64868935bbd503f61bf65
138 N880263831ef148a2819e0cc500084b8b schema:name pubmed_id
139 schema:value 30801055
140 rdf:type schema:PropertyValue
141 N9f561351e4ce45deaac5b3bb6527915d rdf:first N41b50dae1fff447e85bf792efc9d535c
142 rdf:rest N6462bf63adda4c0d8bbbea21fb076d43
143 Nabe1c6b756a945a3be183e409d8941cf rdf:first Nf710b4bb10e3490f9391f246296c04ac
144 rdf:rest rdf:nil
145 Nb62e51beb01f41ec92d8b27e3f86df7b schema:name nlm_unique_id
146 schema:value 101740243
147 rdf:type schema:PropertyValue
148 Nb7d6bb3b93f6462c8df1b246705de7a7 rdf:first Nc32afcdb929f410195b61edcf9b40517
149 rdf:rest N58a8c3da7af04038b76a30f0b0b0d37b
150 Nba7fd73cf48a407d832b3fea0cbe452c schema:affiliation https://www.grid.ac/institutes/grid.7445.2
151 schema:familyName de Marvao
152 schema:givenName Antonio
153 rdf:type schema:Person
154 Nc32afcdb929f410195b61edcf9b40517 schema:affiliation https://www.grid.ac/institutes/grid.7445.2
155 schema:familyName Cook
156 schema:givenName Stuart A
157 rdf:type schema:Person
158 Nc68241ddbb744cf0b61c4bd57ad6c066 schema:volumeNumber 1
159 rdf:type schema:PublicationVolume
160 Nc6b53bbb29f44ce3b6a2935dbc7ad410 rdf:first Nba7fd73cf48a407d832b3fea0cbe452c
161 rdf:rest N35ccb34c7fee4593a4305323815b7f83
162 Ne9c256c106e64868935bbd503f61bf65 rdf:first N0e0d99b8204540a3a8ff8624eb4b701b
163 rdf:rest Nc6b53bbb29f44ce3b6a2935dbc7ad410
164 Nea10e99233304ceebf4c40734b5711c4 schema:affiliation https://www.grid.ac/institutes/grid.7445.2
165 schema:familyName Duan
166 schema:givenName Jinming
167 rdf:type schema:Person
168 Nf144b0aacf3d44ef8f4c3a94b4ce30ef schema:affiliation https://www.grid.ac/institutes/grid.417895.6
169 schema:familyName Howard
170 schema:givenName Luke S G E
171 rdf:type schema:Person
172 Nf710b4bb10e3490f9391f246296c04ac schema:affiliation https://www.grid.ac/institutes/grid.7445.2
173 schema:familyName O'Regan
174 schema:givenName Declan P
175 rdf:type schema:Person
176 Nfc9a620a75824718b44ca73182d0954b schema:affiliation https://www.grid.ac/institutes/grid.7445.2
177 schema:familyName Wilkins
178 schema:givenName Martin R
179 rdf:type schema:Person
180 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
181 schema:name Information and Computing Sciences
182 rdf:type schema:DefinedTerm
183 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
184 schema:name Artificial Intelligence and Image Processing
185 rdf:type schema:DefinedTerm
186 sg:grant.7441966 http://pending.schema.org/fundedItem sg:pub.10.1038/s42256-019-0019-2
187 rdf:type schema:MonetaryGrant
188 sg:journal.1336255 schema:issn 2522-5839
189 schema:name Nature Machine Intelligence
190 rdf:type schema:Periodical
191 sg:pub.10.1007/978-0-85729-057-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035503987
192 https://doi.org/10.1007/978-0-85729-057-1
193 rdf:type schema:CreativeWork
194 sg:pub.10.1007/978-1-4899-4541-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109705929
195 https://doi.org/10.1007/978-1-4899-4541-9
196 rdf:type schema:CreativeWork
197 sg:pub.10.1007/978-3-030-00934-2_52 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107019541
198 https://doi.org/10.1007/978-3-030-00934-2_52
199 rdf:type schema:CreativeWork
200 sg:pub.10.1007/978-3-319-20309-6_1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051381581
201 https://doi.org/10.1007/978-3-319-20309-6_1
202 rdf:type schema:CreativeWork
203 sg:pub.10.1007/978-3-319-46478-7_51 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048275737
204 https://doi.org/10.1007/978-3-319-46478-7_51
205 rdf:type schema:CreativeWork
206 sg:pub.10.1007/978-3-319-51237-2_2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036650911
207 https://doi.org/10.1007/978-3-319-51237-2_2
208 rdf:type schema:CreativeWork
209 sg:pub.10.1007/s00330-016-4217-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035858680
210 https://doi.org/10.1007/s00330-016-4217-6
211 rdf:type schema:CreativeWork
212 sg:pub.10.1007/s10278-013-9604-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002405103
213 https://doi.org/10.1007/s10278-013-9604-9
214 rdf:type schema:CreativeWork
215 sg:pub.10.1007/s10741-017-9621-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086044150
216 https://doi.org/10.1007/s10741-017-9621-8
217 rdf:type schema:CreativeWork
218 sg:pub.10.1007/s11063-018-9828-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103170271
219 https://doi.org/10.1007/s11063-018-9828-2
220 rdf:type schema:CreativeWork
221 sg:pub.10.1038/nature21056 schema:sameAs https://app.dimensions.ai/details/publication/pub.1074217286
222 https://doi.org/10.1038/nature21056
223 rdf:type schema:CreativeWork
224 sg:pub.10.1038/s41598-017-11817-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091665734
225 https://doi.org/10.1038/s41598-017-11817-6
226 rdf:type schema:CreativeWork
227 sg:pub.10.1038/s41598-017-12539-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091866095
228 https://doi.org/10.1038/s41598-017-12539-5
229 rdf:type schema:CreativeWork
230 sg:pub.10.1186/1532-429x-15-35 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041103373
231 https://doi.org/10.1186/1532-429x-15-35
232 rdf:type schema:CreativeWork
233 sg:pub.10.1186/1532-429x-15-91 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052338964
234 https://doi.org/10.1186/1532-429x-15-91
235 rdf:type schema:CreativeWork
236 sg:pub.10.1186/s12874-018-0482-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101228878
237 https://doi.org/10.1186/s12874-018-0482-1
238 rdf:type schema:CreativeWork
239 sg:pub.10.1186/s12968-018-0471-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1106947579
240 https://doi.org/10.1186/s12968-018-0471-x
241 rdf:type schema:CreativeWork
242 https://app.dimensions.ai/details/publication/pub.1109705929 schema:CreativeWork
243 https://doi.org/10.1001/jama.1982.03320430047030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038627387
244 rdf:type schema:CreativeWork
245 https://doi.org/10.1002/(sici)1097-0258(19960229)15:4<361::aid-sim168>3.0.co;2-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037104179
246 rdf:type schema:CreativeWork
247 https://doi.org/10.1002/sim.4780140108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033373649
248 rdf:type schema:CreativeWork
249 https://doi.org/10.1016/j.compeleceng.2018.04.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103638844
250 rdf:type schema:CreativeWork
251 https://doi.org/10.1016/j.csbj.2016.11.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004091080
252 rdf:type schema:CreativeWork
253 https://doi.org/10.1016/j.healun.2017.02.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083902372
254 rdf:type schema:CreativeWork
255 https://doi.org/10.1016/j.ijcard.2017.10.106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092460981
256 rdf:type schema:CreativeWork
257 https://doi.org/10.1016/j.jacbts.2016.11.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086367886
258 rdf:type schema:CreativeWork
259 https://doi.org/10.1016/j.jacc.2014.07.979 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013047510
260 rdf:type schema:CreativeWork
261 https://doi.org/10.1016/j.jbi.2016.10.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028115945
262 rdf:type schema:CreativeWork
263 https://doi.org/10.1016/j.media.2013.04.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004187867
264 rdf:type schema:CreativeWork
265 https://doi.org/10.1016/j.media.2015.08.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021754446
266 rdf:type schema:CreativeWork
267 https://doi.org/10.1016/j.media.2017.06.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086028023
268 rdf:type schema:CreativeWork
269 https://doi.org/10.1016/j.media.2017.07.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090904008
270 rdf:type schema:CreativeWork
271 https://doi.org/10.1080/01621459.1983.10477973 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058302834
272 rdf:type schema:CreativeWork
273 https://doi.org/10.1093/aje/kwu140 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017703766
274 rdf:type schema:CreativeWork
275 https://doi.org/10.1093/bioinformatics/btr511 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012366359
276 rdf:type schema:CreativeWork
277 https://doi.org/10.1093/bioinformatics/btr597 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005259061
278 rdf:type schema:CreativeWork
279 https://doi.org/10.1093/ehjci/jey120 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106005374
280 rdf:type schema:CreativeWork
281 https://doi.org/10.1093/eurheartj/ehv317 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019762568
282 rdf:type schema:CreativeWork
283 https://doi.org/10.1093/eurheartj/ehv510 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059576710
284 rdf:type schema:CreativeWork
285 https://doi.org/10.1093/gigascience/gix005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083568878
286 rdf:type schema:CreativeWork
287 https://doi.org/10.1097/hco.0000000000000491 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093120736
288 rdf:type schema:CreativeWork
289 https://doi.org/10.1098/rsif.2017.0387 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103153059
290 rdf:type schema:CreativeWork
291 https://doi.org/10.1101/222208 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092851007
292 rdf:type schema:CreativeWork
293 https://doi.org/10.1109/42.796284 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061170839
294 rdf:type schema:CreativeWork
295 https://doi.org/10.1109/cvpr.2017.173 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095849007
296 rdf:type schema:CreativeWork
297 https://doi.org/10.1109/icnn.1995.488968 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093669333
298 rdf:type schema:CreativeWork
299 https://doi.org/10.1109/tmi.2019.2894322 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111636818
300 rdf:type schema:CreativeWork
301 https://doi.org/10.1111/j.2517-6161.1972.tb00899.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1110457843
302 rdf:type schema:CreativeWork
303 https://doi.org/10.1145/3071178.3071208 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090598380
304 rdf:type schema:CreativeWork
305 https://doi.org/10.1145/3123266.3130141 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092535212
306 rdf:type schema:CreativeWork
307 https://doi.org/10.1146/annurev-bioeng-071516-044442 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084228312
308 rdf:type schema:CreativeWork
309 https://doi.org/10.1148/radiol.2016161315 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009835081
310 rdf:type schema:CreativeWork
311 https://doi.org/10.1155/2016/6795352 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038688614
312 rdf:type schema:CreativeWork
313 https://doi.org/10.1155/2017/1279486 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092232528
314 rdf:type schema:CreativeWork
315 https://doi.org/10.1161/circimaging.114.002107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017679243
316 rdf:type schema:CreativeWork
317 https://doi.org/10.1161/circresaha.117.311312 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091132987
318 rdf:type schema:CreativeWork
319 https://doi.org/10.1161/circulationaha.114.010637 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011345305
320 rdf:type schema:CreativeWork
321 https://doi.org/10.1183/16000617.0108-2016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084249681
322 rdf:type schema:CreativeWork
323 https://doi.org/10.1371/journal.pcbi.1006076 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103203182
324 rdf:type schema:CreativeWork
325 https://doi.org/10.1371/journal.pone.0110243 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031720838
326 rdf:type schema:CreativeWork
327 https://doi.org/10.1371/journal.pone.0180944 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090670297
328 rdf:type schema:CreativeWork
329 https://doi.org/10.15420/cfr.2016:25:2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085310347
330 rdf:type schema:CreativeWork
331 https://doi.org/10.4018/978-1-60566-900-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1096031471
332 rdf:type schema:CreativeWork
333 https://doi.org/10.7326/m14-0698 schema:sameAs https://app.dimensions.ai/details/publication/pub.1073742342
334 rdf:type schema:CreativeWork
335 https://www.grid.ac/institutes/grid.417895.6 schema:alternateName Imperial College Healthcare NHS Trust
336 schema:name Imperial College Healthcare NHS Trust, London, UK.
337 National Heart and Lung Institute, Imperial College London, UK.
338 rdf:type schema:Organization
339 https://www.grid.ac/institutes/grid.7445.2 schema:alternateName Imperial College London
340 schema:name Department of Computing, Imperial College London, UK.
341 Division of Experimental Medicine, Department of Medicine, Imperial College London, UK.
342 MRC London Institute of Medical Sciences, Imperial College London,UK.
343 National Heart Centre Singapore, Singapore, and Duke-NUS Graduate Medical School, Singapore.
344 National Heart and Lung Institute, Imperial College London, UK.
345 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...